立场：医学人工智能忽视真实治疗结局
Position: Medical AI Neglects Real Treatment Outcomes
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中文总结 AI 辅助
本论文指出医学AI忽视真实治疗结局，当前依赖文本而非真实数据，导致模型与基准存在缺陷，提出应将真实治疗结局纳入训练评估，并将优化其作为医学AI的下游目标。
中文摘要 AI 辅助
医学人工智能已在诊断和预后任务上快速提升性能，这类任务可支撑治疗决策，但模型对治疗本身的理解仍训练不足、评估不到位，目前依赖人类观点和综合内容（尤其是生物医学文献、临床实践指南等文本），而非治疗结局的真实基础数据。这种忽视严重限制了医学人工智能的潜力，前沿模型和主流基准已出现缺陷，本立场论文对此进行了论述。应将来自观察性数据库、随机试验等来源的真实治疗结局大量纳入训练和评估，且需重新强调优化治疗结局作为所有医学人工智能的下游目标。
英文摘要
Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding of treatment itself is still inadequately trained and evaluated, using human opinions and syntheses (especially texts such as biomedical publications and clinical practice guidelines) rather than actual underlying data on treatment outcomes. This neglect seriously limits the potential of medical AI, and is already causing deficiencies in both frontier models and major benchmarks, as argued in this position paper. Real treatment outcomes, drawn from sources such as observational databases and randomized experiments, should be substantially incorporated into both training and evaluation. Improving these outcomes should be reemphasized as the downstream goal of all medical AI.
